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Rumination is a passive form of negative self-focused cognition that predicts depressive episodes for individuals with bipolar disorder (BD). Individuals with BD also have impaired inhibitory executive control; rumination in BD may therefore reflect executive dysfunction. We investigated the relationship between a neural measure of executive functioning (functional connectivity between the frontoparietal control network [FPCN] and the default mode network [DMN] during an effortful task), behavioural measures of executive functioning (the Behavior Rating Inventory of Executive Function) and rumination (the Ruminative Responses Scale).
Fifteen individuals with BD and fifteen healthy controls underwent MRI scans during mental distraction. Using CONN toolbox, between-network FPCN-DMN connectivity values were calculated. We conducted Pearson’s r bivariate correlations between connectivity values, BRIEF and RRS scores.
RRS scores were positively correlated with BRIEF Behavioral Regulation Index (BRI) scores. In individuals with BD, there was a positive correlation between FPCN-DMN functional connectivity during distraction and BRIEF BRI scores. FPCN-DMN functional connectivity was also positively correlated with RRS ruminative brooding scores. Healthy controls did not show significant correlations between these behavioural and neural measures of executive functioning and rumination.
For individuals with BD, the greater the tendency to ruminate and the higher the executive dysfunction, the stronger the connectivity between an executive control network and a network involved in rumination during an unrelated cognitive task. This could reflect continual attempts to inhibit ruminative thinking and shift back to the distraction task. Therefore, engagement in rumination may reflect failed inhibitory executive control.
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Over the years, attempts have been made to classify depressive syndromes based on various criteria. For several decades, the term reactive depression was used to describe cases involving an obvious precipitant, whereas endogenous depression lacked a recent stressor. Alternatively, the term secondary depression has been used in reference to cases related to a defined medical condition, as opposed to examples of primary depression. In the current classification scheme, the Diagnostic and Statistics Manual of Mental Disorders, 5th edition (DSM-V) lists fifteen distinct diagnoses related to disorders of mood, which are shown in Table 4.1. Eight of these disorders are considered depressive disorders, whereas seven categorize patients within the bipolar spectrum of illness.
Depression is a disorder that causes disability, with a profound adverse impact on all areas of psychosocial functioning. This is particularly true for those with treatment resistant depression (TRD). However, to date, no systematic assessments of psychosocial functioning for patients with TRD have been conducted.
In the present study, we used the Longitudinal Interval Follow-up Evaluation (LIFE) scale to measure psychosocial functioning in 92 patients with TRD. These patients met formal criteria for TRD and were part of a clinical trial examining the efficacy of lithium augmentation of nortriptyline.
Clinicians rated this sample of patients as experiencing mild to moderate impairment in work-related activities, good to fair interpersonal relations, poor level of involvement in recreational activities, and mild impairment of ability to enjoy sexual activity. Patients and clinicians rated global social adjustment as poor.
Patients with formally defined TRD experience significant impairment in psychosocial functioning. In this sample a tendency existed for both clinicians and patients to assign more severely impaired global ratings when compared with ratings for specific functional areas.
Immune system markers may predict affective disorder treatment response, but whether an overall immune system marker predicts bipolar disorder treatment effect is unclear.
Bipolar CHOICE (N = 482) and LiTMUS (N = 283) were similar comparative effectiveness trials treating patients with bipolar disorder for 24 weeks with four different treatment arms (standard-dose lithium, quetiapine, moderate-dose lithium plus optimised personalised treatment (OPT) and OPT without lithium). We performed secondary mixed effects linear regression analyses adjusted for age, gender, smoking and body mass index to investigate relationships between pre-treatment white blood cell (WBC) levels and clinical global impression scale (CGI) response.
Compared to participants with WBC counts of 4.5–10 × 109/l, participants with WBC < 4.5 or WBC ≥ 10 showed similar improvement within each specific treatment arm and in gender-stratified analyses.
An overall immune system marker did not predict differential treatment response to four different treatment approaches for bipolar disorder all lasting 24 weeks.
Immunological theories, particularly the sickness syndrome theory, may explain psychopathology in mood disorders. However, no clinical trials have investigated the association between overall immune system markers with a wide range of specific symptoms including potential gender differences.
We included two similar clinical trials, the lithium treatment moderate-dose use study and clinical and health outcomes initiatives in comparative effectiveness for bipolar disorder study, enrolling 765 participants with bipolar disorder. At study entry, white blood cell (WBC) count was measured and psychopathology assessed with the Montgomery and Aasberg depression rating scale (MADRS). We performed analysis of variance and linear regression analyses to investigate the relationship between the deviation from the median WBC, and multinomial regression analysis between different WBC levels. All analyses were performed gender-specific and adjusted for age, body mass index, smoking, race, and somatic diseases.
The overall MADRS score increased significantly for each 1.0×109/l deviation from the median WBC among 322 men (coefficient=1.10; 95% CI=0.32–1.89; p=0.006), but not among 443 women (coefficient=0.56; 95% CI=−0.19–1.31; p=0.14). Among men, WBC deviations were associated with increased severity of sadness, inner tension, reduced sleep, reduced appetite, concentration difficulties, inability to feel, and suicidal thoughts. Among women, WBC deviations were associated with increased severity of reduced appetite, concentration difficulties, lassitude, inability to feel, and pessimistic thoughts. Both higher and lower WBC levels were associated with increased severity of several specific symptoms.
Immune system alterations were associated with increased severity of specific mood symptoms, particularly among men. Our results support the sickness syndrome theory, but furthermore emphasise the relevance to study immune suppression in bipolar disorder. Due to the explorative nature and cross-sectional design, future studies need to confirm these findings.
Little is known about predictors of recovery from bipolar depression.
We investigated affective instability (a pattern of frequent and large mood shifts over time) as a predictor of recovery from episodes of bipolar depression and as a moderator of response to psychosocial treatment for acute depression.
A total of 252 out-patients with DSM-IV bipolar I or II disorder and who were depressed enrolled in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) and were randomised to one of three types of intensive psychotherapy for depression (n = 141) or a brief psychoeducational intervention (n = 111). All analyses were by intention-to-treat.
Degree of instability of symptoms of depression and mania predicted a lower likelihood of recovery and longer time until recovery, independent of the concurrent effects of symptom severity. Affective instability did not moderate the effects of psychosocial treatment on recovery from depression.
Affective instability may be a clinically relevant characteristic that influences the course of bipolar depression.
There are a variety of barriers to major depressive disorder (MDD) treatment which may impede access to professional help and prevent best patient outcome. These barriers include the heterogeneity of MDD, societal factors, and clinician-patient relationship as well as issues of concordance, compliance, and adherence.
Understanding the epidemiology of major depressive disorder (MDD) and the neurobiologic theories behind depression and antidepressant treatment is vital for physicians who must identify and treat patients with this disorder. The epidemiology of MDD reveals that this disorder is widespread: the lifetime prevalence of MDD is estimated to be ∼17% and the 12-month prevalence is ≥7%, according to the National Comorbidity Survey Replication. Epidemiologic studies suggest that in any 30-day period, 2% to 5% of the United States population meet criteria for MDD. In addition, nearly twice as many women as men (21% versus 13%, respectively) are affected by a depressive disorder during their lifetimes. These numbers reveal a vast population of people affected by MDD, making depression a tremendous social and medical concern.